Parallel real time computation of large scale pedestrian evacuations

Usually, modeling of the evacuations is done during the planning and authorizing process of office buildings or large scale facilities, where computing time is not an issue at all. The collaborative Hermes project [1] aims at improving the safety of mass events by constructing an evacuation assistant, a decision support system for heads of operation in an actual evacuation. For this, the status (occupancy and available egress routes) of a facility is constantly monitored with automatic person counters, door sensors, smoke sensors, and manual input from security staff. Starting from this status, egress is simulated faster than real time, and the result visualized in a suitable fashion to show what is likely to happen in the next 15 min. The test case for this evacuation assistant is the clearing of the ESPRIT Arena in Düsseldorf which holds 50,000–65,000 persons depending on the event type. The on site prediction requires the ability to simulate the egress in ≈2 min, a task that requires the combination of a fast algorithm and a parallel computer. The paper will describe the details of the evacuation problem, the architecture of the evacuation assistant, the pedestrian motion model employed and the optimization and parallelization of the code.     

HCCI柴油机燃烧过程数值模拟研究

均质压燃(HCCI:homgeneous charge compression ignition)燃烧方式已成为发动机领域的硬件研究热点。针对CA4D32-09型柴油机,对HCCI燃烧过程进行了计算模拟,并利用统计的方法对影响HCCI燃烧特性的因素进行分析,通过计算得出HCCI发动机燃烧过程的影响因素及发动机主要参数对混合气均质特性的作用。     

Adaptive histogram-based filter for image restoration

A novel filter for image restoration is proposed in this paper. The filter estimates histogram of original image via input image. It gets a membership function through the histogram, and the membership function contains a lot of information of original image. Then a weighted fuzzy mean filter is established based on this membership function; meanwhile, the filter adaptively adopts different filter scale according to the character divergence of image region and intensity of impulsive noise. Experimental result shows that new filter gives superior performance to conventional filters and currently used fuzzy filter.     

Optimization techniques in respiratory control system models

One of the most complex physiological systems whose modeling is still an open study is the respiratory control system where different models have been proposed based on the criterion of minimizing the work of breathing (WOB). The aim of this study is twofold: to compare two known models of the respiratory control system which set the breathing pattern based on quantifying the respiratory work; and to assess the influence of using direct-search or evolutionary optimization algorithms on adjustment of model parameters. This study was carried out using experimental data from a group of healthy volunteers under CO₂ incremental inhalation, which were used to adjust the model parameters and to evaluate how much the equations of WOB follow a real breathing pattern. This breathing pattern was characterized by the following variables: tidal volume, inspiratory and expiratory time duration and total minute ventilation. Different optimization algorithms were considered to determine the most appropriate model from physiological viewpoint. Algorithms were used for a double optimization: firstly, to minimize the WOB and secondly to adjust model parameters. The performance of optimization algorithms was also evaluated in terms of convergence rate, solution accuracy and precision. Results showed strong differences in the performance of optimization algorithms according to constraints and topological features of the function to be optimized. In breathing pattern optimization, the sequential quadratic programming technique (SQP) showed the best performance and convergence speed when respiratory work was low. In addition, SQP allowed to implement multiple non-linear constraints through mathematical expressions in the easiest way. Regarding parameter adjustment of the model to experimental data, the evolutionary strategy with covariance matrix and adaptation (CMA-ES) provided the best quality solutions with fast convergence and the best accuracy and precision in both models. CMAES reached the best adjustment because of its good performance on noise and multi-peaked fitness functions. Although one of the studied models has been much more commonly used to simulate respiratory response to CO₂ inhalation, results showed that an alternative model has a more appropriate cost function to minimize WOB from a physiological viewpoint according to experimental data.     

Large scale automatic image annotation based on convolutional neural network

Automatic image annotation is one of the most important challenges in computer vision, which is critical to many real-world researches and applications. In this paper, we focus on the issue of large scale image annotation with deep learning. Firstly, considering the existing image data, especially the network images, most of the labels of themselves are inaccurate or imprecise. We propose a Multitask Voting (MV) method, which can improve the accuracy of original annotation to a certain extent, thereby enhancing the training effect of the model. Secondly, the MV method can also achieve the adaptive label, whereas most existing methods pre-specify the number of tags to be selected. Additionally, based on convolutional neural network, a large scale image annotation model MVAIACNN is constructed. Finally, we evaluate the performance with experiments on the MIRFlickr25K and NUS-WIDE datasets, and compare with other methods, demonstrating the effectiveness of the MVAIACNN.     

Feasibility study of fuel cell-hybrid powertrains in non-road mobile machineries

This paper presents a powertrain feasibility study for a fuel cell-hybrid non-road mobile machinery. The objective is to compare different fuel cell-hybrid powertrain topologies. Different hybrid topologies define the powertrain from a source to loads. The compared properties of different powertrains are weight, size, efficiency, initial cost, and lifetime cost. The paper assesses topologies with different active and passive connections of a battery pack, ultracapacitor pack, or both. The comparison of the topologies needs a validated simulation tool, specific power control algorithms, and detailed knowledge about the target application. The results conclude that fuel cell powertrains buffered with a high-power battery pack yield minimum weight and size. However, minimum lifetime costs are obtained by topologies buffered with an ultracapacitor, which, on the other hand, increase weight and size. Furthermore, there are cost effective buffering combinations of a battery and ultracapacitor in comparison to topologies buffered with a high-energy battery.     

Prediction of hourly energy consumption in buildings based on a feedback artificial neural network

In this paper a new approach for short-term load prediction in buildings is shown. The method is based on a special kind of artificial neural network (ANN), which feeds back a part of its outputs. This ANN is trained by means of a hybrid algorithm. The new system uses current and forecasted values of temperature, the current load and the hour and the day as inputs. The performance of this predictor was evaluated using real data and results from international contests. The achieved results demonstrate the high precision reached with this system.     

Applications of an all-polymer solution-processed high-performance,transistor

Low cost production and a simple manufacturing process are desired for organic field effect transistors, OFETs, even though this usually significantly lowers device performance. We report on applications and characterization of a high performance polymer field effect transistor that utilizes a hygroscopic insulator. The latter enables the device to be used as a sensor element to detect the presence of various solvents. We show a drastic change in conductivity and current modulation for some organic solvents. Furthermore, the use of the device in electric circuits is demonstrated by combining hygroscopic insulator field effect transistor (HIFET) devices in an inverter/amplifier circuit.     

基于全寿命周期成本的高速铁路连续梁桥减隔震方案研究

高烈度地震区大跨连续梁桥一般采用减隔震设计以减小结构地震反应及由此带来的经济损失,而不同的减隔震方案亦决定着结构的抗震性能和初始成本。为了从抗震性能和经济性两方面综合评价连续梁桥减隔震设计的合理性,以一座高烈度区高速铁路连续梁桥为背景,设计多种减隔震布置方案开展桥梁地震易损性分析,并基于全寿命周期成本最小准则探讨了桥梁在全寿命期内各方案的经济性。结果表明：全桥采用双曲面球型支座和黏滞阻尼器配合使用方案在全寿命周期的地震损失最小,但初始成本过高致其经济性较差;全桥布置双曲面球型支座、边墩配置黏滞阻尼器方案满足抗震设防要求且全寿命周期成本最小,为本文减隔震设计中的最优方案。本文提出的桥梁减隔震方案研究方法实现了结构抗震性能与其经济效益的双目标评价,为利益相关者对桥梁减隔震方案的合理决策提供了新思路。